At Koyeb, we're working to build the most seamless way to deploy apps to production without worrying about infrastructure. But there's still plenty to keep you busy at the application layer with performance tuning and troubleshooting. That's why we're introducing Metrics — an easy way to monitor and troubleshoot application performance. Deploying on Koyeb makes thinking about infrastructure or orchestration unnecessary.
In the first two parts of our Inside Flink blog series, we explored the benefits of stream processing with Flink and common Flink use cases for which teams are choosing to leverage the popular framework to unlock the full potential of streaming. Specifically, we broke down the key reasons why developers are choosing Apache Flink® as their stream processing framework, as well as the ways in which they are putting it into practice.
Artificial intelligence has the potential to make work incredibly efficient—which means it’s the perfect complement to process automation technology. Process automation, and related approaches like business process management, already aim to improve productivity by automating what can and should be automated.
We're thrilled to announce enhancements to Xcode test reporting in Bitrise, designed to improve your mobile app debugging workflow. For iOS developers, our new Beta feature allows for better visibility into Xcode test results on the Build Details page.
One of the most important questions in architecting a data platform is where to store and archive data. In a blog series, we’ll cover the different storage strategies for Kafka and introduce you to Lenses’ S3 Connector for backup/restore. But in this first blog, we must introduce the different Cloud storage options available. Later blogs will focus on specific solutions, explain in more depth how this maps to Kafka and then how Lenses manage your Kafka topic backups.
What are the differences between generative AI vs. large language models? How are these two buzzworthy technologies related? In this article, we’ll explore their connection. To help explain the concept, I asked ChatGPT to give me some analogies comparing generative AI to large language models (LLMs), and as the stand-in for generative AI, ChatGPT tried to take all the personality for itself.
